@inproceedings{shen-shen-2025-auto,
    title = "Auto-{SLURP}: A Benchmark Dataset for Evaluating Multi-Agent Frameworks in Smart Personal Assistant",
    author = "Shen, Lei  and
      Shen, Xiaoyu",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.596/",
    doi = "10.18653/v1/2025.findings-emnlp.596",
    pages = "11163--11174",
    ISBN = "979-8-89176-335-7",
    abstract = "In recent years, multi-agent frameworks powered by large language models (LLMs) have advanced rapidly. Despite this progress, there is still a notable absence of benchmark datasets specifically tailored to evaluate their performance. To bridge this gap, we introduce Auto-SLURP, a benchmark dataset aimed at evaluating LLM-based multi-agent frameworks in the context of smart personal assistants. Auto-SLURP extends the original SLURP dataset{---}initially developed for natural language understanding tasks{---}by relabeling the data and integrating simulated servers and external services. This enhancement enables a comprehensive end-to-end evaluation pipeline, covering language understanding, task execution, and response generation. Our experiments demonstrate that Auto-SLURP presents a significant challenge for current state-of-the-art frameworks, highlighting that truly reliable and intelligent multi-agent personal assistants remain a work in progress."
}